Table Motion Detection in Interventional Coronary Angiography

Abstract

The most common method for detecting coronary artery stenosis is interventional coronary angiography (ICA). However, 2D angiography has limitations because it displays complex 3D structures of arteries as 2D X-ray projections. To overcome these limitations, 3D models or tomographic images of the arterial tree can be reconstructed from 2D projections. The 3D modeling process of the arterial tree requires accurate acquisition geometry since in many ICA acquisitions the patient table is translated to cover the entire area of interest, the original calibrated geometry is no longer valid for the 3D reconstruction process. This study presents methods for identifying the frames acquired during table translation in an angiographic scene. Spatio-temporal methods based on deep learning were used to identify translated frames. Three different architectures3D convolutional neural network (CNN), bidirectional convolutional long short term memory (CONVLSTM), and fusion of bi-directional CONVLSTM and 3D CNNwere trained and tested. The combination of CONVLSTM and 3D CNN surpasses the other two methods and achieves a macro f1-score (mean f1-scores of two classes) of 93%.